mean = np.mean(data) std = np.std(data) threshold = 3 * std data = data[abs(data - mean) < threshold]代码翻译
时间: 2024-05-14 16:12:35 浏览: 20
这段代码的功能是:
1. 计算数据的平均值(mean)和标准差(std)。
2. 计算阈值(threshold),其值为3倍的标准差。
3. 从数据中筛选出绝对值大于等于(abs)平均值与阈值的乘积(mean * threshold)的数据。
具体翻译如下:
```
mean = np.mean(data) # 计算数据的平均值
std = np.std(data) # 计算数据的标准差
threshold = 3 * std # 计算阈值,即3倍的标准差
data = data[abs(data - mean) < threshold] # 从数据中筛选出绝对值小于阈值的数据
```
其中,`np`是numpy库的别名,`mean`、`std`和`data`分别代表数据的平均值、标准差和原始数据。
相关问题
使用C++ eigen库翻译以下python代码import pandas as pd import numpy as np import time import random def main(): eigen_list = [] data = [[1,2,4,7,6,3],[3,20,1,2,5,4],[2,0,1,5,8,6],[5,3,3,6,3,2],[6,0,5,2,19,3],[5,2,4,9,6,3]] g_csi_corr = np.cov(data, rowvar=True) #print(g_csi_corr) eigenvalue, featurevector = np.linalg.eigh(g_csi_corr) print("eigenvalue:",eigenvalue) eigen_list.append(max(eigenvalue)) #以下代码验证求解csi阈值 eigen_list.append(1.22) eigen_list.append(-54.21) eigen_list.append(8.44) eigen_list.append(-27.83) eigen_list.append(33.12) #eigen_list.append(40.29) print(eigen_list) eigen_a1 = np.array(eigen_list) num1 = len(eigen_list) eigen_a2 = eigen_a1.reshape((-1, num1)) eigen_a3 = np.std(eigen_a2, axis=0) eigen_a4 = eigen_a3.tolist() k = (0.016 - 0.014) / (max(eigen_a4) - min(eigen_a4)) eigen_a5 = [0.014 + k * (i - min(eigen_a4)) for i in eigen_a4] tri_threshold = np.mean(eigen_a5)
#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
std::vector<double> eigen_list;
MatrixXd data(6, 6);
data << 1, 2, 4, 7, 6, 3,
3, 20, 1, 2, 5, 4,
2, 0, 1, 5, 8, 6,
5, 3, 3, 6, 3, 2,
6, 0, 5, 2, 19, 3,
5, 2, 4, 9, 6, 3;
MatrixXd g_csi_corr = data.transpose() * data / 6.0;
EigenSolver<MatrixXd> es(g_csi_corr);
VectorXd eigenvalue = es.eigenvalues().real();
std::cout << "eigenvalue: " << eigenvalue.transpose() << std::endl;
eigen_list.push_back(eigenvalue.maxCoeff());
eigen_list.push_back(1.22);
eigen_list.push_back(-54.21);
eigen_list.push_back(8.44);
eigen_list.push_back(-27.83);
eigen_list.push_back(33.12);
//eigen_list.push_back(40.29);
std::cout << "eigen_list: ";
for (std::vector<double>::iterator it = eigen_list.begin(); it != eigen_list.end(); ++it)
std::cout << *it << " ";
std::cout << std::endl;
int num1 = eigen_list.size();
MatrixXd eigen_a2 = Map<MatrixXd>(eigen_list.data(), num1, 1);
VectorXd eigen_a3 = eigen_a2.array().rowwise().mean().transpose();
VectorXd eigen_a4 = (eigen_a2 - eigen_a3.replicate(num1, 1)).array().abs().rowwise().mean().transpose();
double k = 0.002 / (eigen_a4.maxCoeff() - eigen_a4.minCoeff());
VectorXd eigen_a5 = 0.014 + k * (eigen_a4.array() - eigen_a4.minCoeff());
double tri_threshold = eigen_a5.mean();
std::cout << "tri_threshold: " << tri_threshold << std::endl;
return 0;
}
def handler(context, event): context.logger.info("Run yolo-v8 model") data = event.body buf = io.BytesIO(base64.b64decode(data["image"])) threshold = float(data.get("threshold", 0.35)) context.user_data.model.conf = threshold image = Image.open(buf) yolo_results = context.user_data.model(image, conf=threshold)[0] labels = yolo_results.names detections = sv.Detections.from_yolov8(yolo_results) detections = detections[detections.confidence > threshold] boxes = detections.xyxy conf = detections.confidence class_ids = detections.class_id results = [] if boxes.shape[0] > 0: for label, score, box in zip(class_ids, conf, boxes): xtl = int(box[0]) ytl = int(box[1]) xbr = int(box[2]) ybr = int(box[3]) results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": [xtl, ytl, xbr, ybr], "type": "rectangle",}) return context.Response(body=json.dumps(results), headers={}, content_type='application/json', status_code=200)改成yolov8分割模型
以下是根据您提供的代码,修改为适应 YOLOv8 分割模型的示例代码:
```python
import io
import json
import base64
from PIL import Image
import torch
import torchvision.transforms as transforms
from torchvision import models
def handler(context, event):
context.logger.info("Run yolo-v8 segmentation model")
data = event.body
buf = io.BytesIO(base64.b64decode(data["image"]))
threshold = float(data.get("threshold", 0.35))
# 加载模型
model = models.segmentation.deeplabv3_resnet50(pretrained=True)
model.eval()
# 图像预处理
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = transform(Image.open(buf)).unsqueeze(0)
# 模型推理
with torch.no_grad():
output = model(image)['out']
output_predictions = torch.argmax(output.squeeze(), dim=0).detach().cpu().numpy()
# 处理分割结果
results = []
for class_id in range(1, output_predictions.max() + 1):
mask = (output_predictions == class_id).astype(int)
contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
results.append({
"label": str(class_id),
"points": [int(x), int(y), int(x + w), int(y + h)],
"type": "rectangle"
})
return context.Response(body=json.dumps(results), headers={},
content_type='application/json', status_code=200)
```
此示例假设您正在使用 PyTorch 和 torchvision 库,并使用了预训练的 DeepLabv3_ResNet50 模型进行分割任务。代码中包括模型加载、图像预处理、模型推理和结果处理的步骤。请注意,具体的修改可能需要根据您使用的模型和环境进行调整。
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